Context-based grading

ABSTRACT

A driving analysis server may be configured to receive vehicle operation data from mobile devices respectively disposed within the vehicles, and may use the data to group the vehicles into multiple groups. A driving pattern for each vehicle may be established and compared against a group driving pattern of its corresponding group to identify outliers. A driver score the outliers may be adjusted positively or negatively based on whether the outlier behaved in a manner more or less safe than its group. Further, unsafe driving events performed by the outlier that were the result of another vehicle&#39;s unsafe driving event may be ignored or positively accounted for in determining or adjusting the outlier&#39;s driver score.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.17/583,904, filed Jan. 25, 2022, which is a continuation of U.S. patentapplication Ser. No. 16/847,709, filed Apr. 14, 2020, now issued as U.S.Pat. No. 11,260,873, which is a continuation of U.S. patent applicationSer. No. 16/014,417, filed Jun. 21, 2018, now issued as U.S. Pat. No.10,633,022, which is a continuation U.S. patent application Ser. No.15/081,135, filed Mar. 25, 2016, now issued as U.S. Pat. No. 10,029,696,which are incorporated herein by reference in their entirety.

TECHNICAL FIELD

Aspects of the disclosure generally relate to a framework for adjustingdriver scores for vehicles that have a driving pattern substantiallydifferent from a driving pattern of a group of vehicles while alsoaccounting for less safe driving behavior associated with vehicles ofthe group.

BACKGROUND

The collection and analysis of driving data, such as the identificationof driving behaviors and traffic accidents, has many applications. Forexample, insurance companies and financial institutions may offer ratediscounts or other financial incentives to customers based on safedriving behaviors and accident-free driving records. Law enforcement orgovernment personnel may collect and analyze driving data and trafficaccident statistics to identify dangerous driving roads or times, and todetect moving violations and other unsafe driving behaviors. In othercases, driving data may be used for navigation applications, vehicletracking and monitoring applications, and on-board vehicle maintenanceapplications, among others.

Vehicle-based computer systems, such as on-board diagnostics (OBD)systems and telematics devices, may be used in automobiles and othervehicles, and may be capable of collecting various driving data andvehicle sensor data. For example, OBD systems may receive informationfrom the vehicle's on-board computers and sensors in order to monitor awide variety of information relating to the vehicle systems, such asengine RPM, emissions control, vehicle speed, throttle position,acceleration and braking rates, use of driver controls, etc. Vehiclesmay also include Global Positioning System (GPS) receivers and devicesinstalled within or operating at the vehicle configured to collectvehicle location and time data. Such vehicle-based systems may becapable of collecting driving data which may be used to perform variousdriving data analyses such as statistical driving evaluations, driverscore calculations, etc. Vehicle-based systems also may be configured todetect the occurrence of traffic accidents, for instance, using vehiclebody impact sensors and airbag deployment sensors. However, not allvehicles are equipped with systems capable of collecting, analyzing, andcommunicating driving data. Moreover, a single vehicle may be used bymultiple different drivers, and conversely, a single driver may drivemultiple different vehicles. Thus, vehicle driving data and/or accidentrecords collected by vehicle-based systems might not include the vehicleoccupants that correspond to the collected driving and accident data.

In contrast to vehicle-based systems, mobile devices such assmartphones, personal digital assistants, tablet computers, and thelike, are often carried and/or operated by a single user. Some mobiledevices may include movement sensors, such as an accelerometer,gyroscope, speedometer, and/or GPS receivers, capable of detectingmovement.

SUMMARY

The following presents a simplified summary in order to provide a basicunderstanding of some aspects of the disclosure. The summary is not anextensive overview of the disclosure. It is neither intended to identifykey or critical elements of the disclosure nor to delineate the scope ofthe disclosure. The following summary merely presents some concepts ofthe disclosure in a simplified form as a prelude to the descriptionbelow.

Aspects of the disclosure relate to methods, computer-readable media,and apparatuses for receiving telematics data (e.g., vehicle operationdata, driver data, environmental data, etc.) associated with one or morevehicles from one or more mobile devices respectively disposed withinthe one or more vehicles. Telematics data may include, for example, ageographic location of the vehicle, a route being traversed by thevehicle, driving events or maneuvers (e.g., braking, turning,accelerating) performed by the vehicle, corresponding timestamps ortimeframes, etc. In some instances, the telematics data may includesensor data from various sensors operationally coupled to the vehicleand configured to sense the immediate surroundings of the vehicle. Acomputing device may group the vehicles based on their telematics data.For instance, vehicles may be grouped based on route traversed,geographic proximity with one another, etc. The computing device mayalso use telematics data to determine driving patterns representative ofdriving behaviors of each vehicle as well as group driving patternsrepresentative of the normalized driving behavior for a group ofvehicles. One or more outlier vehicles for each group may be identifiedbased on the outlier vehicles' driving pattern being different from itsgroup's group driving pattern beyond a dissimilarity metric threshold(or a similarity metric threshold). A driver score of the outliervehicle may be positively or negatively adjusted based on whether theoutlier was safer or less safe than its group. A measure of safety for avehicle may be a number of unsafe events (e.g., hard-braking, swerving,excessive speeding, etc.) determined from a vehicle's telematics datafor a particular time period. A measure of safety for a group ofvehicles may be an average number of unsafe events determined from thetelematics data of the vehicles in the group for the particular timeperiod.

In accordance with further aspects of the present disclosure, contextualdata (e.g., extenuating circumstances) may be considered in determininga driver score. For instance, a high-risk or unsafe driving eventperformed by a vehicle and identified from the vehicle's telematics datamay generally result in a negative adjustment to the driver's score.However, if the driver performed the high-risk or unsafe driving eventto avoid an accident with another vehicle that is behaving badly (e.g.,performing a high-risk or unsafe driving event), the driver's scoremight not be negatively adjusted. Rather, the driver score may bemaintained or positively adjusted. As an example, a driver who performsa hard-braking event as a result of going too fast on a curve may resultin a negative adjustment to the driver's score. However, a driver whoperforms a hard-braking event to avoid a collision with another vehiclethat is swerving and cutting off the driver's vehicle might not resultin a negative adjustment to the driver's score. In some instances, sucha driver may receive a positive adjustment to his or her driving score.

In accordance with further aspects of the present disclosure, acomputing device may determine whether a vehicle that was not involvedin a collision or accident determination but nonetheless caused thecollision or accident.

Other features and advantages of the disclosure will be apparent fromthe additional description provided herein.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of the present invention and theadvantages thereof may be acquired by referring to the followingdescription in consideration of the accompanying drawings, in which likereference numbers indicate like features, and wherein:

FIG. 1 depicts an illustrative network environment and computing systemsthat may be used to implement aspects of the disclosure.

FIG. 2 depicts an illustrative diagram of a driving analysis system,according to one or more aspects of the disclosure.

FIG. 3 depicts an illustrative flow diagram for an example method ofgrouping vehicles and identifying outliers that have a driving patternsubstantially different from the group's driving pattern, according toone or more aspect of the disclosure.

FIG. 4 depicts an illustrative flow diagram for an example method ofadjusting a driving score of an identified outlier driver that has adriving pattern substantially different from the group's drivingpattern, according to one or more aspect of the disclosure.

FIG. 5 depicts an illustrative flow diagram for an example method ofaccounting for less safe driving behavior of other vehicles indetermining a driver score for an outlier, according to one or moreaspect of the disclosure.

FIGS. 6 and 7 depict an illustrative top view of multiple vehiclestraversing a street or road, according to one or more aspects of thedisclosure.

FIG. 8 depicts an illustrative flow diagram for an example method ofdetermining whether a vehicle not involved in an accident caused theaccident, according to one or more aspects of the disclosure.

DETAILED DESCRIPTION

In the following description of the various embodiments, reference ismade to the accompanying drawings, which form a part hereof, and inwhich is shown by way of illustration, various embodiments of thedisclosure that may be practiced. It is to be understood that otherembodiments may be utilized.

As will be appreciated by one of skill in the art upon reading thefollowing disclosure, various aspects described herein may be embodiedas a method, a computer system, or a computer program product.Accordingly, those aspects may take the form of an entirely hardwareembodiment, an entirely software embodiment or an embodiment combiningsoftware and hardware aspects. Furthermore, such aspects may take theform of a computer program product stored by one or morecomputer-readable storage media having computer-readable program code,or instructions, embodied in or on the storage media. Any suitablecomputer readable storage media may be utilized, including hard disks,CD-ROMs, optical storage devices, magnetic storage devices, and/or anycombination thereof. In addition, various signals representing data orevents as described herein may be transferred between a source and adestination in the form of electromagnetic waves traveling throughsignal-conducting media such as metal wires, optical fibers, and/orwireless transmission media (e.g., air and/or space).

FIG. 1 illustrates a block diagram of a computing device (or system) 101in communication system 100 that may be used according to one or moreillustrative embodiments of the disclosure. The device 101 may have aprocessor 103 for controlling overall operation of the device 101 andits associated components, including RAM 105, ROM 107, input/outputmodule 109, and memory 115. The computing device 101, along with one ormore additional devices (e.g., terminals 141, 151) may correspond to anyof multiple systems or devices, such as a driving analysis server orsystem, configured as described herein for receiving and analyzingvehicle driving data and calculating driver scores based on identifieddriving events.

Input/Output (I/O) 109 may include a microphone, keypad, touch screen,and/or stylus through which a user of the computing device 101 mayprovide input, and may also include one or more of a speaker forproviding audio output and a video display device for providing textual,audiovisual and/or graphical output. Software may be stored withinmemory 115 and/or storage to provide instructions to processor 103 forenabling device 101 to perform various functions. For example, memory115 may store software used by the device 101, such as an operatingsystem 117, application programs 119, and an associated internaldatabase 121. Processor 103 and its associated components may allow thedriving analysis system 101 to execute a series of computer-readableinstructions to receive driving data from a vehicle, identify a drivingevent based on the driving data, receive image data, video data, and/orobject proximity data associated with the driving event, and perform ananalysis of the driving event based on image data, video data, and/orobject proximity data.

The driving analysis system 101 may operate in a networked environment100 supporting connections to one or more remote computers, such asterminals 141 and 151. The terminals 141 and 151 may be personalcomputers, servers (e.g., web servers, database servers), or mobilecommunication devices (e.g., vehicle telematics devices, on-boardvehicle computers, mobile phones, portable computing devices, and thelike), and may include some or all of the elements described above withrespect to the driving analysis system 101. The network connectionsdepicted in FIG. 1 include a local area network (LAN) 125 and a widearea network (WAN) 129, and a wireless telecommunications network 133,but may also include other networks. When used in a LAN networkingenvironment, the driving analysis system 101 may be connected to the LAN125 through a network interface or adapter 123. When used in a WANnetworking environment, the system 101 may include a modem 127 or othermeans for establishing communications over the WAN 129, such as network131 (e.g., the Internet). When used in a wireless telecommunicationsnetwork 133, the system 101 may include one or more transceivers,digital signal processors, and additional circuitry and software forcommunicating with wireless computing devices 141 (e.g., mobile phones,vehicle telematics devices) via one or more network devices 135 (e.g.,base transceiver stations) in the wireless network 133.

It will be appreciated that the network connections shown areillustrative and other means of establishing a communications linkbetween the computers may be used. The existence of any of variousnetwork protocols such as TCP/IP, Ethernet, FTP, HTTP and the like, andof various wireless communication technologies such as GSM, CDMA, WiFi,and WiMAX, is presumed, and the various computing devices and drivinganalysis system components described herein may be configured tocommunicate using any of these network protocols or technologies.

Additionally, one or more application programs 119 used by the drivinganalysis server/system 101 may include computer executable instructions(e.g., driving analysis programs, pattern identification and analysisalgorithms, and driver score algorithms) for receiving vehicle drivingdata, identifying driving events, retrieving additional image data,video data, and/or object proximity data associated with the drivingevents, analyzing the driving events and the additional associated data,performing driving data analyses or driver score computations for one ormore vehicles or drivers, and performing other related functions asdescribed herein.

As used herein, a driver score (or driving score) may refer to ameasurement of driving abilities, safe driving habits, and other driverinformation. A driver score may be a rating generated by an insuranceprovider, financial instruction, or other organization, based on thedriver's age, vision, medical history, driving record, and/or otheraccount data relating to the driver. For example, an insurance providerserver 101 may periodically calculate driver scores for one or more ofthe insurance provider's customers, and may use the driver scores toperform insurance analyses and determinations (e.g., determine coverage,calculate premiums and deductibles, award safe driver discounts, etc.).As discussed herein, the driver score may be increased or decreasedbased on real-time data collected by vehicle sensors, telematicsdevices, and other systems for measuring driving performance. Forexample, if a driver consistently drives within posted speed limits,wears a seatbelt, and keeps the vehicle in good repair, the driver scoremay be positively adjusted (e.g., increased). Alternatively, if a driverregularly speeds, drives aggressively, and does not properly maintainthe vehicle, the driver score may be negatively adjusted (e.g.,decreased). It should be understood that a driver score, as used herein,may be associated with an individual, group of individuals, or avehicle. For instance, a family, group of friends or co-workers, orother group that shares a vehicle, may have a single driver score thatis shared by the group. Additionally, a vehicle may have an associateddriver score that is based on one or more primary drivers of the vehicleand can be affected by the driving behavior of any the vehicle'sdrivers. In other examples, a vehicle may be configured to identifydifferent drivers, and each driver of the vehicle may have a separatedriver score.

FIG. 2 is a diagram of an illustrative driving analysis system 200. Eachcomponent shown in FIG. 2 may be implemented in hardware, software, or acombination of the two. Additionally, each component of the drivinganalysis system 200 may include a computing device (or system) havingsome or all of the structural components described above for computingdevice 101.

The driving analysis system 200 shown in FIG. 2 includes a vehicle 210,such as an automobile, motorcycle, boat, scooter, or other vehicle forwhich a driving event data analysis may be performed and for which adriver score may be calculated. The vehicle 210 may include vehicleoperation sensors 212 capable of detecting and recording telematics data(e.g., various conditions at the vehicle and operational parameters ofthe vehicle). For example, sensors 212 may detect and store datacorresponding to the vehicle's speed, distances driven, rates ofacceleration or braking, and specific instances of sudden acceleration,braking, and swerving. Sensors 212 also may detect and store datareceived from the vehicle's 210 internal systems, such as impact to thebody of the vehicle, air bag deployment, headlights usage, brake lightoperation, door opening and closing, door locking and unlocking, cruisecontrol usage, hazard lights usage, windshield wiper usage, horn usage,turn signal usage, seat belt usage, phone and radio usage within thevehicle, maintenance performed on the vehicle, and other data collectedby the vehicle's computer systems.

Additional sensors 212 may detect and store the external drivingconditions, which may also be referred to herein as telematics data,(for example, external temperature, rain, snow, light levels, and sunposition for driver visibility). Sensors 212 also may detect and storetelematics data relating to moving violations and the observance oftraffic signals and signs by the vehicle 210. Additional sensors 212 maydetect and store telematics data relating to the maintenance of thevehicle 210, such as the engine status, oil level, engine coolanttemperature, odometer reading, the level of fuel in the fuel tank,engine revolutions per minute (RPMs), and/or tire pressure.

The vehicle 210 also may include one or more cameras and proximitysensors 214 capable of recording additional conditions inside or outsideof the vehicle 210, which may also be referred to herein as telematicsdata. Internal cameras 214 may detect conditions such as the number ofthe passengers in the vehicle 210, and potential sources of driverdistraction within the vehicle (e.g., pets, phone usage, unsecuredobjects in the vehicle). External cameras and proximity sensors 214 maydetect other nearby vehicles, traffic levels, road conditions, trafficobstructions, animals, cyclists, pedestrians, and other conditions thatmay factor into a driving event data analysis.

The operational sensors 212 and the cameras and proximity sensors 214may store data within the vehicle 210, and/or may transmit the data toone or more external computer systems (e.g., a vehicle operationcomputer system 225 and/or a driving analysis server 220). As shown inFIG. 2 , the operation sensors 212, and the cameras and proximitysensors 214, may be configured to transmit data to a vehicle operationcomputer system 225 via a telematics device 216. In other examples, oneor more of the operation sensors 212 and/or the cameras and proximitysensors 214 may be configured to transmit data directly without using atelematics device 216. For example, telematics device 216 may beconfigured to receive and transmit data from operational sensors 212,while one or more cameras and proximity sensors 214 may be configured todirectly transmit data to a vehicle operation computer system 225 or adriving analysis server 220 without using the telematics device 216.Thus, telematics device 216 may be optional in certain embodiments whereone or more sensors or cameras 212 and 214 within the vehicle 210 may beconfigured to independently capture, store, and transmit vehicleoperation and driving data.

Telematics device 216 may be a computing device containing many or allof the hardware/software components as the computing device 101 depictedin FIG. 1 . As discussed above, the telematics device 216 may receivevehicle operation and driving data (e.g., telematics data) from vehiclesensors 212, and proximity sensors and cameras 214, and may transmit thetelematics data to one or more external computer systems (e.g., avehicle operation computer system 225 and/or a driving analysis server220) over a wireless transmission network. Telematics device 216 alsomay be configured to detect or determine additional types of telematicsdata relating to real-time driving and the condition of the vehicle 210.In certain embodiments, the telematics device 216 may contain or may beintegral with one or more of the vehicle sensors 212 and proximitysensors and cameras 214 discussed above, and/or with one or moreadditional sensors discussed below.

Additionally, the telematics device 216 may be configured to collecttelematics data regarding the number of passengers and the types ofpassengers (e.g. adults, children, teenagers, pets, etc.) in the vehicle210. The telematics device 216 also may be configured to collecttelematics data indicating a driver's movements or the condition of adriver. For example, the telematics device 216 may include orcommunicate with sensors that monitor a driver's movements, such as thedriver's eye position and/or head position, etc. Additionally, thetelematics device 216 may collect data regarding the physical or mentalstate of the driver, such as fatigue or intoxication. The condition ofthe driver may be determined through the movements of the driver orthrough sensors, for example, sensors that detect the content of alcoholin the air or blood alcohol content of the driver, such as abreathalyzer.

The telematics device 216 also may collect telematics data regarding thedriver's route choice, whether the driver follows a given route, and toclassify the type of trip (e.g. commute, errand, new route, etc.). Incertain embodiments, the telematics device 216 may be configured tocommunicate with the sensors and/or cameras 212 and 214 to determinewhen and how often the vehicle 210 stays in a single lane or strays intoother lanes. To determine the vehicle's route, lane position, and otherdata, the telematics device 216 may include or may receive data from amobile telephone, a Global Positioning System (GPS), locational sensorspositioned inside a vehicle, or locational sensors or devices remotefrom the vehicle 210.

The telematics device 216 also may store the type of the vehicle 210,for example, the make, model, trim (or sub-model), year, and/or enginespecifications. The vehicle type may be programmed into the telematicsdevice 216 by a user or customer, determined by accessing a remotecomputer system, such as an insurance company or financial institutionserver, or may be determined from the vehicle itself (e.g., by accessingthe vehicle's 210 computer systems).

The vehicle 210 also may include a personal mobile device 218 containinga number of software and hardware components. A personal mobile device218 may be located within a vehicle 210, such as a driver's orpassenger's smartphone, tablet computer, or other personal mobiledevice. As used herein, a mobile device 218 “within” a vehicle 210refers to a mobile device 218 that is inside of or otherwise secured toa moving vehicle, for instance, mobile devices 218 in the cabins ofautomobiles, buses, recreational vehicles, mobile devices 218 travelingin open-air vehicles such as motorcycles, scooters, or boats, and mobiledevices 218 in the possession of drivers or passengers of vehicles 210.Mobile devices 220 may be, for example, smartphones or other mobilephones, personal digital assistants (PDAs), tablet computers, and thelike, and may include some or all of the elements described above withrespect to the computing device 101.

A mobile device 218 may be configured to establish communication withvehicle-based devices (e.g., sensors, on-board vehicle computingdevices, etc.) and various internal components of vehicle 210 viawireless networks or wired connections (e.g., for docked devices),whereby such mobile devices 218 may have secure access to internalvehicle sensors 212, camera/proximity sensors 214 and othervehicle-based systems. However, in other examples, mobile device 218might not connect to vehicle-based computing devices and internalcomponents, but may operate independently by communicating with vehicles210 via standard communication interfaces (e.g., short-rangecommunication systems, telematics devices 216, etc.), indirectly throughexternal networks and servers, or might not communicate at all withvehicles 210.

Mobile devices 218 each may include a network interface, which mayinclude various network interface hardware (e.g., adapters, modems,wireless transceivers, etc.) and software components to enable mobiledevices 218 to communicate with external servers (e.g., driving analysisserver 220), vehicles 210, and various other external computing devices.One or more specialized software applications, such as a telematics dataacquisition application may be stored in the memory of the mobile device220. Application(s) may be received via network interface from drivinganalysis server 220, vehicles 210, or other application providers (e.g.,public or private application stores). Certain applications might notinclude user interface screens, while other applications may includeuser interface screens that support user interaction. Such applicationsmay be configured to run as user-initiated applications or as backgroundapplications. The memory of mobile device 218 also may include databasesconfigured to receive and store accident data, vehicle data, driver orpassenger data, insurance data, and the like, associated with one ormore drivers and/or vehicles. Although this section describes varioussoftware application(s) as executing on mobile devices 218, in variousother implementations, some or all of the functionality described hereinmay be implemented within the vehicle 210, via specialized hardwareand/or software applications within a vehicle-based system, such assoftware within a telematics device 216 or a vehicle control computer,etc.

Like the vehicle-based computing devices in vehicles 210, mobile devices218 also may include various components configured to sense (e.g.,generate or acquire) telematics data (e.g., geographic location,heading, route, linear velocity, angular velocity, acceleration,deceleration, driver data, weather data, and/or other telematics datadiscussed herein) and transmit the telematics data or other relevantdata to driving analysis server 220 for determination of outliers anddriver scores as discussed in further detail below. For example, usingdata from movement sensors (e.g., 1-axis, 2-axis, or 3-axisaccelerometers, compasses, speedometers, vibration sensors, gyroscopicsensors, etc.) and/or GPS receivers or other location-based services(LBS), an application of mobile device 218 may determine that the mobiledevice 218 is in a moving vehicle, that a driving trip has started orstopped, and/or that a vehicle accident has occurred. The movementsensors and/or GPS receiver or LBS component of a mobile device 218 mayalso be used to determine driving speeds, routes, accident force andangle of impact, and other accident characteristics and accident-relateddata.

The vehicle 210 and the personal mobile device 218 may communicate witheach other via wireless networks or wired connections (e.g., for devicesphysically docked in vehicles), and each may communicate with one ormore additional vehicles 210, additional mobile computing devices,and/or a number of external computer servers (e.g., driving analysisserver 220) over one or more communication networks (e.g., cellularcommunication network, WiFi communication network, etc.). The sensordata also may be transmitted from vehicles 210 via a telematics device216 or other network interface(s) to one or more remote computingdevices, such as one or more personal mobile devices 218 and/or externalservers (e.g., driving analysis server 220). For example, mobilecomputing device 218 may transmit telematics data, driver data, vehicledata (e.g., braking, linear acceleration, angular velocity, etc.),directly to driving analysis server 220, and thus may be used inconjunction with or instead of telematics devices 216. Additionally,mobile computing devices 218 (and/or telematics device 216) may beconfigured to perform the vehicle-to-vehicle (V2V) andvehicle-to-infrastructure (V2I) communications described below, byestablishing connections and transmitting/receiving telematics data toand from other nearby vehicles. Thus, mobile computing device 218(and/or telematics device 216) may be used in conjunction with, orinstead of, a short-range communication system, which is describedfurther below. In addition, mobile computing device 218 may be used inconjunction with the vehicle control computers for purposes of vehiclecontrol and diagnostics.

Short-range communication systems may be vehicle-based data transmissionsystems configured to transmit various (e.g., driving data, vehicledata, insurance data, driver and passenger data, etc.) to other nearbyvehicles, and to receive corresponding data from other nearby vehicles.In some examples, communication systems may use the dedicatedshort-range communications (DSRC) protocols and standards to performwireless communications between vehicles. In the United States, 75 MHzin the 5.850-5.925 GHz band have been allocated for DSRC systems andapplications, and various other DSRC allocations have been defined inother countries and jurisdictions. However, short-range communicationsystems need not use DSRC, and may be implemented using othershort-range wireless protocols in other examples, such as WLANcommunication protocols (e.g., IEEE 802.11), Bluetooth (e.g., IEEE802.15.1), or one or more of the Communication Access for Land Mobiles(CALM) wireless communication protocols and air interfaces. The V2Vtransmissions between the short-range communication systems may be sentvia DSRC, Bluetooth, satellite, GSM infrared, IEEE 802.11, WiMAX, RFID,and/or any suitable wireless communication media, standards, andprotocols. In certain systems, short-range communication systems mayinclude specialized hardware installed in vehicles 210 (e.g.,transceivers, antennas, etc.), while in other examples the communicationsystems may be implemented using existing vehicle hardware components(e.g., radio and satellite equipment, navigation computers) or may beimplemented by software running on the mobile devices 218 of drivers andpassengers within the vehicles 210.

V2V communications also may include vehicle-to-infrastructure (V2I)communications, such as transmissions from vehicles to non-vehiclereceiving devices, for example, toll booths, rail road crossings, androad-side traffic monitoring devices. Certain V2V communication systemsmay periodically broadcast data from a vehicle 210 to any other vehicle,or other infrastructure device capable of receiving the communication,within the range of the vehicle's transmission capabilities. The rangeof V2V communications and V2I communications may depend on the wirelesscommunication standards and protocols used, the transmission/receptionhardware (e.g., transceivers, power sources, antennas), and otherfactors. Short-range V2V (and V2I) communications may range from just afew feet to many miles, and different types of telematics data andcharacteristics may be determined depending on the range of the V2Vcommunications.

Vehicle operation computer system 225 may be a computing device separatefrom the vehicle 210, containing some or all of the hardware/softwarecomponents as the computing device 101 depicted in FIG. 1 . The vehicleoperation computer system 225 may be configured to receive and store thevehicle operation data discussed above from vehicle 210, and similarvehicle operation data from one or more other vehicles 210 a-n. In theexample shown in FIG. 2 , the vehicle operation computer system 225includes a vehicle operation database 227 that may be configured tostore the vehicle operation data collected from the vehicle sensors 212,proximity sensors and cameras 214, and telematics devices 216 of aplurality of vehicles. The vehicle operation database 227 may storeoperational sensor data, proximity sensor data, camera data (e.g.,image, audio, and/or video), location data and/or time data for multiplevehicles 210.

Data stored in the vehicle operation database 227 may be organized inany of several different manners. For example, a table in the vehicleoperation database 227 may contain all of the vehicle operation data fora specific vehicle 210, similar to a vehicle event log. Other tables inthe vehicle operation database 227 may store certain types of data formultiple vehicles. For instance, tables may store specific drivingbehaviors (e.g., driving speed, acceleration and braking rates,swerving, tailgating, use of seat belts, turn signals or other vehiclecontrols, etc.) for multiple vehicles 210 at specific locations, such asspecific neighborhoods, roads, or intersections. Vehicle operation datamay also be organized by time, so that the driving events or behaviorsof multiple vehicles 210 may be stored or grouped by time (e.g.,morning, afternoon, late night, rush hour, weekends, etc.) as well aslocation.

The system 200 also may include a driving analysis server 220,containing some or all of the hardware/software components as thecomputing device 101 depicted in FIG. 1 . The driving analysis server220 may include hardware, software, and network components to receivevehicle operation data from the vehicle operation computer system 225and/or directly from a plurality of vehicles 210 and/or a plurality ofmobile devices respectively disposed within the plurality of vehicles.The driving analysis server 220 and the vehicle operation computersystem 225 may be implemented as a single server/system, or may beseparate servers/systems. In some examples, the driving analysis server220 may be a central server configured to receive vehicle operation datafrom a plurality of remotely located vehicle operation computer systems225.

As shown in FIG. 2 , driving analysis server 220 may include a drivinganalysis module 221 and a driver score calculation module 222. Modules221 and 222 may be implemented in hardware and/or software configured toperform a set of specific functions within the driving analysis server220. For example, the driving analysis module 221 and the driver scorecalculation module 222 may include one or more driving eventanalysis/driver score calculation algorithms, driving patterndetermination and comparison algorithms, which may be executed by one ormore software applications running on generic or specialized hardwarewithin the driving analysis server 220. The driving analysis module 221may use the vehicle operation data received from the vehicle operationcomputer system 225 and/or additional image data, video data, and/orobject proximity data to perform driving event analyses for vehicles210. The driver score calculation module 222 may use the results of thedriving event analysis performed by module 221 to calculate or adjust adriver score for a driver of a vehicle 210 based on specific drivingevents. Further descriptions and examples of the algorithms, functions,and analyses that may be executed by the driving analysis server 220 aredescribed below in reference to FIGS. 3-8 .

To perform driving event analyses and driver score calculations, thedriving analysis server 220 may initiate communication with and/orretrieve data from one or more mobile devices, one or more vehicles 210,vehicle operation computer systems 225, and additional computer systems231-234 storing data that may be relevant to the driving event analysesand driver score calculations. For example, one or more traffic datastorage systems 231, such as traffic databases, may store datacorresponding to the amount of traffic and certain trafficcharacteristics (e.g., amount of traffic, average driving speed, trafficspeed distribution, and numbers and types of accidents, etc.) at variousspecific locations and times. Traffic data storage systems 231 also maystore image and video data recorded by traffic cameras various specificlocations and times. One or more weather data storage systems 232, suchas weather databases, may store weather data (e.g., rain, snow, sleet,hail, temperature, wind, road conditions, visibility, etc.) at differentlocations and different times. One or more additional drivingdatabases/systems 233 may store additional driving data from one or moredifferent data sources or providers which may be relevant to the drivingevent analyses and/or driver score calculations performed by the drivinganalysis server 220. Additional driving databases/systems 233 may storedata regarding events such as road hazards and traffic accidents, downedtrees, power outages, road construction zones, school zones, and naturaldisasters that may affect the driving event analyses and/or driver scorecalculations performed by the driving analysis server 220. One or moreadditional infrastructure systems 234 may store sensor data collectedfrom traffic lights, traffic signs (e.g., stop signs), and other trafficstructures (e.g., bus stops, train stations, construction sites, othertraffic signs, etc.). For instance, various traffic lights and trafficsigns may include sensors (e.g., motion sensors, cameras, etc.) that maybe used to obtain driving characteristics of a vehicle. As an example, astop sign may detect how quickly a vehicle is decelerating and whetherthe vehicle stopped at a specified point. The sensor may then transmit,to a central controller of the infrastructure system 234 via a wide areanetwork (e.g., satellite network, cellular network, etc.), the sensordata along with a time stamp. In some cases, sensor data may beperiodically transferred to the central controller. Additionally oralternative, sensor data may be transferred in response to detection ofan event (e.g., a vehicle approaching a traffic light or traffic sign).As discussed below in reference to FIGS. 3-8 , the driving analysisserver 220 may retrieve and use data from databases/systems 231-234 toanalyze and evaluate the driving events identified from the driving dataof vehicles 210.

FIG. 3 depicts an illustrative flow diagram for an example method ofgrouping vehicles and identifying outliers that have a driving patternsubstantially different from the group's driving pattern, according toone or more aspect of the disclosure. The method of FIG. 3 and/or one ormore steps thereof may be performed by a computing device (e.g., drivinganalysis server 220). The method illustrated in FIG. 3 and/or one ormore steps thereof may be partially or fully embodied, for example, incomputer-executable instructions that are stored in a computer-readablemedium, such as a non-transitory computer-readable memory. In someinstances, one or more steps of FIG. 3 may be performed in a differentorder and/or combined. In some instances, one or more steps of FIG. 3may be omitted and/or otherwise not performed.

The steps in the example method of FIG. 3 describe a method ofidentifying outlier drivers who follow a driving pattern substantiallydifferent from other drivers under similar circumstances. For instance,some outlier drivers may drive in a more low-risk and safe manner ascompared to other drivers under similar circumstances. For instance,some outlier drivers may drive in a more high-risk and unsafe manner ascompared to other drivers under similar circumstances. Suchdeterminations (e.g., whether an outlier driver is more or less safethan his or her peers) may be used to adjust a driver score of theoutlier driver as will be discussed in further detail below.Additionally, and as will be explained in further detail below,contextual information may explain why the outlier driver is behavingdifferently from his or her peers, which may also be accounted for inthe driver's score.

As shown in FIG. 3 , the method may begin at step 302 in which acomputing device (e.g., driving analysis server 220) may receivetelematics data from one or more (e.g., multiple) vehicles 210 a-n. Asan example, the telematics data may be received from one or more of thevehicle operation sensors 212, telematics device 216, and/or camera andproximity sensors 214. Devices 212-216 may begin transmitting telematicsdata in response to the vehicle being turned on, being moved, or someother triggering event defined by an operator driving analysis server220 and sent to the devices 212-216.

In one or more arrangements, mobile device 218 may receive telematicsdata of a vehicle from one or more of vehicle's vehicle operationsensors 212, telematics device 216, and camera and proximity sensors214, and relay that information to driving analysis server 220.Additionally or alternatively, mobile device 218 may generate telematicsdata using its own processor, GPS component, accelerometer, gyroscope,camera, microphone and other sensor devices of mobile device 218. Insome instances, an application (e.g., mobile app) installed on mobiledevice 218 may detect when it (and, thus, its user or owner) have beguna trip in a moving vehicle and record acceleration, deceleration,lateral movement, geographic location and other telematics data ofmobile device 218, which may correlate and/or otherwise represent thetelematics data of the vehicle 210 in which mobile device 218 isdisposed and/or otherwise located within. Mobile device 218 may detectthat a trip has begun in response to determining that mobile device ismoving beyond a preset speed (e.g., 20 mph) for a preset duration (e.g.,2 minutes).

In some cases, one or more vehicles might not send its telematics datato driving analysis server 220. For instance, such vehicle's vehicleoperations sensors 212, telematics device 216, camera/proximity sensors214, and mobile device 218 might not send the vehicle's telematics data.In such cases, camera and proximity sensors 214 of a vehicle that aretransmitting the telematics data of the vehicle may include imagesand/or video data of vehicles in its immediate surroundings, whichdriving analysis server 220 may use to determine the location,acceleration, deceleration, and other telematics data of those vehiclesin the image and/or video data. As a result, driving analysis server 220may receive telematics data from some vehicles and derive telematicsdata of vehicles that do not send their telematics data.

In certain embodiments, telematics devices 216, vehicle operationsystems 225, mobile devices 218 and other data sources may transmittelematics data (e.g., vehicle operation data) for a vehicle 210 to thedriving analysis server 220 in real-time (or near real-time). Thedriving analysis server 220 may be configured to receive the vehicleoperation data, and then perform real-time (or near real-time) drivinganalyses and driver score calculations for the vehicle 210. In otherembodiments, vehicle operation data might not be transmitted inreal-time but may be sent periodically (e.g., hourly, daily, weekly,etc.) by telematics devices 216 or vehicle operation systems 225.Periodic transmissions of vehicle operation data may include data for asingle vehicle or single driver, or for multiple vehicles or drivers.The driving analysis server 220 may be configured to receive theperiodic transmissions, and then to perform the steps of FIGS. 3-5 and 8.

Telematics data may include, for example, sensor data, geographiclocation data, linear acceleration data, linear deceleration data,linear velocity, lateral orientation, angular rotational velocity,angular rotational acceleration, orientation relative to Earth'smagnetic field, image data, video data, audio data, route, street name,navigational directions, status of components of the vehicle (e.g.,brake activated), or any other data describing the behavior of thevehicle discussed herein.

Additionally, driving analysis server 220 may receive telematics data ofvehicle from one or more infrastructure systems. For example, trafficstructures such as traffic lights and traffic signs (e.g., stop signs)may include one or more sensors for capturing telematics data of avehicle when the vehicle is within a preset distance of the trafficstructure. The sensors may include motions sensors, cameras, and othersensors described herein. The sensors may transmit, to the drivinganalysis server 220, the telematics data along with timestamp at whichthe data was collected, the location of the traffic structure, a statusof the traffic light (e.g., whether the light was green, yellow or red),and/or an identifier of the traffic structure.

Using the telematics data captured from the sensors, the drivinganalysis server 220 may be able to determine the speed of a vehicle, therate at which the vehicle is accelerating or decelerating (e.g., whetherthe vehicle hard-braked in response to a yellow light), changes indirection of the vehicle (e.g., whether the vehicle swerved), and otherdriving events described herein. In addition, by collecting telematicsfrom multiple vehicles in close proximity to one another, the drivinganalysis server 220 may determine the effects of actions by one vehicleon another vehicle. As an example, the driving analysis server 220 maydetermine that a first vehicle swerved in front of a second vehiclecausing the second vehicle to hard-brake. The driving analysis server220 may determine whether another environmental circumstance caused avehicle to perform an unsafe driving event. As an example, the drivinganalysis server 220 may determine that a vehicle hard-braked when thetraffic light was yellow. As another example, the driving analysisserver 220 may determine that a vehicle reduced speed but did not stopfor a stop sign. As will be discussed more fully below, eachdetermination may result in an increased or decreased driver score forthe driver of the vehicle.

In step 304, a computing device (e.g., driving analysis server 220) maygroup vehicles into one or more groups based on their telematics data.In one or more arrangements, vehicles 210 a-n may be grouped based ontheir geographic location (e.g., to capture vehicles travelling along asame route). The operator of driving analysis server 220 may define amaximum distance in which each vehicle of a group may be separated fromeach other vehicle of a group without being considered as no longer partof the group. As an example, the maximum distance may be 50 feet (or15.24 meters) or some other distance. In this example, a vehicle that isa distance greater than 50 feet from another vehicle considered in thegroup would no longer be considered part of the group. Driving analysisserver 220 may use telematics data (e.g., geographic location, videodata, etc.) of the vehicles to determine a distance between thevehicles.

For instance, FIG. 6 depicts a top view of multiple vehicles 210 a-etraversing a street 602, according to one or more aspects of thedisclosure. Since each of vehicles 210 a-c may be within 50 feet fromeach other, driving analysis server 220 may group vehicles 210 a-c intoone group (e.g., group 604 a). Vehicles 210 d-e might not be groupedinto group 604 a since they are each located at a distance greater than50 feet away from at least one of vehicles 210 a-c. However, drivinganalysis server 220 may group vehicles 210 d-e into another group (e.g.,group 604 b) since they are within 50 feet from one another. An operator(or an external application via API communicating with driving analysisserver 220) may adjust the maximum distance to increase or reduce thegranularity (e.g., size) of the groups.

Additionally or alternatively, in one or more arrangements, vehicles 210a-n may be grouped based on routes traversed by vehicles 210 a-n. Aroute may include one or more streets traversed in a particularsequential order by a vehicle. As an example, a route may include avehicle's traversal of a particular segment of street A in a particulardirection, followed by traversal of a particular segment of street B ina particular direction, and so on. A segment of a street may bespecified by two geographic locations (e.g., two sets of GPS longitudeand latitude coordinates), by two end markers (e.g., two boundingcross-streets that intersect the street), etc. Driving analysis server220 may identify various routes traversed by vehicles 210 a-n and groupvehicles that traversed the same route. In some instances, the groupingmay also be specific to a time of day or date in which the vehicles 210a-n traversed the various routes. In other instances, the groupingsmight not be specific to a time of day or date in which the vehicles 210a-n traversed the routes. As an example, two vehicles that traversed thesame route at different times of day or on different dates may still begrouped together.

In some embodiments, vehicles in a particular group may be furthergrouped into one or more levels of sub-groups based on their telematicsdata. As an example, one group may be formed based on each vehicletraversing the same route. Driving analysis server 220 may group thevehicles of the group into one or more subgroups based on a time of dayor date the vehicles traversed the route. Driving analysis server 220may then group the vehicles of each subgroup into another level ofsubgroups based on the vehicles being within a maximum distance fromeach other in a similar manner as discussed above in connection withFIG. 6 .

In step 306, a computing device (e.g., driving analysis server 220) maydetermine a driving pattern for each of the vehicles 210 a-n based ontheir telematics data and store the driving patterns in a database. Avehicle's driving pattern may be based on, for example, vehicle state oroperation data (e.g., linear acceleration data, linear decelerationdata, linear velocity, lateral orientation, angular rotational velocity,angular rotational acceleration, orientation relative to Earth'smagnetic field, image data, video data, audio data, navigationaldirections (e.g., turn left, turn right), status of components of thevehicle (e.g., brake activated, etc.) and the corresponding geographiclocations, times and/or sequential order at which the vehicle was in theparticular state or performed a particular operation.

As an example, a pattern may be in the form of a vector. A vector mayinclude multiple elements in a specific sequence. The position of eachelement in the vector may represent different geographic locationsand/or times at which the vehicle traversed various points of the route.As an example, the second element in each vector may correspond to whenthe respective vehicle traversed geographic location B of the route. Thepositions in the vector define a sequential order of performed drivingoperations or states. In some instances, an operator may select variouspoints of a route on a geographical map to assign to different positionsof the vector. Each element may represent a driving event or state(e.g., any driving event or state discussed herein). A value of eachelement may represent the type of driving event or state and/or itslevel of risk or safety. For instance, values 1-5 may represent a brakeevent with 1 being a very soft-braking event and 5 being a veryhard-braking event. For instance, values 5-10 may represent anacceleration event with a value of 6 being a gradual acceleration and 10being a rapid acceleration. Driving analysis server 220 may store alegend of what each value of an element represents, which may be in theform of a table. For instance, the legend may indicate that a value of 1represents a soft-brake event.

As another example, each pattern may be in the form of a directed graph.Driving analysis server 220 may, for each of the vehicles 210 a-n,correlate geographic location with one or more other telematics data(e.g., acceleration data, deceleration data, angular rotationalvelocity, etc.) and, based on the determined correlations, generate apattern of state transitions (e.g., soft-brake, accelerate, constantvelocity, left turn, right turn, hard-brake, lane change, swerve, etc.).The pattern of state transitions may be in the form of a directedbehavioral graph including multiple objects (e.g., state) interconnectedby multiple links (e.g., directed edges). The objects may represent aparticular driving event or state (e.g., acceleration, turn left, turnright, soft-brake, hard-brake, lane-change, swerve, etc.) and the linksmay be representative of a temporal or geographic relations between theobjects.

In step 308, a computing device (e.g., driving analysis server 220) maydetermine a group driving pattern for each of the groups and/orsub-groups and store the patterns in a database. For instance, drivinganalysis server 220 may, for each group, compare each group member'sdriving pattern with each other group member's driving pattern todetermine a normalized driving pattern representative of the group'sdriving behavior. Similarly, driving analysis server 220 may, for eachsub-group, compare each sub-group member's driving pattern with eachother sub-group member's driving pattern to determine a normalizeddriving pattern representative of the sub-group's driving behavior.

Following the vectors example, driving analysis server 220 may generatea vector that is representative of a group by averaging each of thevectors associated with vehicles in that group. For instance, drivinganalysis server 220 may average the first element in each vector and setthe result as the value of the first element in the group's orsub-group's vector. Similarly, driving analysis server 220 may averagethe second element in each vector and set the result as the value of thesecond element in the group's or sub-group's vector.

Following the directed graph example, driving analysis server 220 mayinfer and/or otherwise generate a normalized directed graph based on thedirected graph for each vehicle in a group or sub-group. In suchinstances, driving analysis server 220 may select objects and links mostfrequently used at a particular position in each directed graph to bethe objects and links of the normalized graph.

In step 310, a computing device (e.g., driving analysis server 220) mayidentify outlier vehicles (also referred to herein as outlier drivers orsimply outliers). For each member (e.g., vehicle/driver) of a group orsub-group, driving analysis server 220 may compare the vehicle's drivingpattern with its corresponding group's or sub-group's group drivingpattern to determine whether the vehicle's driving pattern is similar ordissimilar from the group's or subgroup's group driving pattern. If thevehicle's driving pattern is dissimilar (e.g., different) from itsgroup's or sub-group's group driving pattern beyond a particularmeasure, the vehicle may be identified as an outlier.

Following the vector example, driving analysis server 220 may compare avehicle's vector with a group's or sub-group's vector. In one instance,driving analysis server 220 may compute a vector score for a vectorbased on the values of each element of the vector. For example, drivinganalysis server 220 may aggregate each of the values in the vector. Thedriving analysis server 220 may then compare the vector score of avehicle with the vector score of its group or sub-group. If theresulting difference between the two vector scores is greater than orequal to a maximum difference threshold (or, alternatively, less than aminimum similar threshold), the vehicle may be identified as an outlier.If not, the vehicle might not be identified as an outlier.

Following the directed graph example, driving analysis server 220 maycompare a vehicle's directed graph with a group's or sub-group'sdirected graph. In one instance, driving analysis server 220 maydetermine the percentage of matching object and/or edge sequences. Ifthe percentage of matching objects and/or edge sequences is less than aminimum similarity threshold percentage, the vehicle may be identifiedas an outlier. If the percentage of matching objects and/or edgesequences is less than the minimum similarity threshold percentage, thevehicle might not be identified as an outlier. Additionally oralternatively, in another instance, driving analysis server 220 maydetermine whether an object type (e.g., a brake event) of a particularor operator-specified object in the vehicle's directed graph matches theobject type of a corresponding object in the group's or sub-group'sdirected graph.

FIG. 4 depicts an illustrative flow diagram for an example method ofadjusting a driving score of an identified outlier driver that has adriving pattern sufficiently different from the group's driving pattern,according to one or more aspect of the disclosure. The method of FIG. 4and/or one or more steps thereof may be performed by a computing device(e.g., driving analysis server 220). The method illustrated in FIG. 4and/or one or more steps thereof may be partially or fully embodied, forexample, in computer-executable instructions that are stored in acomputer-readable medium, such as a non-transitory computer-readablememory. In some instances, one or more steps of FIG. 4 may be performedin a different order and/or combined. In some instances, one or moresteps of FIG. 4 may be omitted and/or otherwise not performed.

In some instances, prior to performing the steps of FIG. 4 , one or moresteps of FIG. 3 may be performed. For example, steps 302-310 may havealready been performed so that the computing device has received thetelematics data of the vehicles and identified outliers.

As shown in FIG. 4 , the method may begin at step 402 in which acomputing device (e.g., driving analysis server 220) may determine adriver score for each of the vehicles 210 a-n based on the telematicsdata of the vehicles. Step 402 may be performed at any point in timeafter the telematics data of the vehicle has been received. For example,step 402 may be performed prior to grouping the vehicles.

A driver score may be determined based on his or her vehicle'stelematics data. In some instances, a person assigned to the vehicle maybe associated with the driver score even if another person drives thevehicle. In other instances, the driver of the vehicle may bespecifically identified so that the driver score is associated with theperson who actually drove the vehicle. Driving analysis server 220 mayidentify high-risk or unsafe events performed by the vehicle from thetelematics data, for example, by comparing values of the telematics datawith preset dynamic thresholds, or combinations of thresholds. Thepreset thresholds may be dynamically adjusted based on weather and roadconditions. For instance, the thresholds may have different values fordry weather conditions, rain, snow, sleet, fog, daytime, night time,road construction, and/or combinations thereof. As an example, a maximumspeed threshold used in determining an excessive speeding event may be50 miles per hour on a particular road during the daytime and when theroad is dry. However, the maximum speed threshold may be 30 miles perhour on the particular road during the night time and when the road iswet (e.g., when it is raining).

The driver score may account for numerous other high-risk or unsafedriving events, which may negatively affect (e.g., reduce) the driverscore. As an example, an excessive speeding event may be a high-risk orunsafe driving event in which the vehicle's speed or velocity is greaterthan a maximum speed threshold (e.g., speed limit of road beingtraversed by the vehicle). As another example, a hard-braking event maybe a high-risk or unsafe driving event in which the vehicle deceleratedat a rate greater than a maximum threshold deceleration rate. As yetanother example, a hard-acceleration event may be a high-risk or unsafedriving event in which the vehicle accelerated at a rate greater than amaximum threshold acceleration rate. As still yet another example, ahard-turning event may be a high-risk or unsafe driving event in whichthe vehicle traveled at an angular rotational velocity greater than amaximum threshold angular rotational velocity. As another example, aswerving event may be a high-risk or unsafe driving event in which thevehicle performed two turns in quick succession. A tailgating event maybe a high-risk or unsafe driving event in which the vehicle followsanother vehicle too closely (e.g., is within a distance that is lessthan minimum distance from the other vehicle). A cutoff event may be ahigh-risk and unsafe driving event in which the vehicle changes lanesthat would have resulted in a collision with another vehicle but for anevasive maneuver of the other vehicle.

At step 404, a computing device (e.g., driving analysis server 220) may,after one or more outliers have been identified in step 310, determinewhether an outlier driver was more or less safe that his or her group orsub-group. Driving analysis server 220 may determine a total number ofhigh-risk or unsafe driving events performed by the outlier and anaverage number of high-risk or unsafe driving events by the group orsub-group. If the outlier has fewer high-risk or unsafe driving eventsthan the group average, then the outlier is driving safer than the othermembers of the group on average and the driving analysis server 220 may,in step 406, positively adjust (e.g., increase) the driver score of theoutlier. Otherwise, if the outlier has a greater number of high-risk orunsafe driving events than the group average, then the outlier isdriving less safe than the other members of the group on average and thedriving analysis server 220 may, in step 408, negatively adjust (e.g.,decrease) the driver score of the outlier.

FIG. 5 depicts an illustrative flow diagram for an example method ofaccounting for less safe driving behavior of other vehicles indetermining a driver score for an outlier, according to one or moreaspect of the disclosure. In some cases, an outlier may have performed ahigh-risk or unsafe driving event in order to avoid an accident withanother vehicle that has suddenly swerved into the path of the outlier.In such cases, the high-risk or unsafe driving event (e.g.,hard-braking) performed by the outlier might be ignored in computing itsdriver score. The method of FIG. 5 and/or one or more steps thereof maybe performed by a computing device (e.g., driving analysis server 220).The method illustrated in FIG. 5 and/or one or more steps thereof may bepartially or fully embodied, for example, in computer-executableinstructions that are stored in a computer-readable medium, such as anon-transitory computer-readable memory. In some instances, one or moresteps of FIG. 5 may be performed in a different order and/or combined.In some instances, one or more steps of FIG. 5 may be omitted and/orotherwise not performed.

In some instances, prior to performing the steps of FIG. 5 , one or moresteps of FIG. 3 or 4 may be performed. For example, steps 302-310 and/orsteps 402-408 may have already been performed so that the computingdevice has received the telematics data of the vehicles and identifiedoutliers. As discussed above, telematics data for the vehicles may alsobe received from sensors (e.g., motion sensors, cameras, etc.) attachedto traffic structures such as traffic lights and traffic signs (e.g.,stop signs).

As shown in FIG. 5 , the method may begin at step 502 in which acomputing device (e.g., driving analysis server 220) may identify, afterone or more outliers have been identified in step 310, times at whichthe outlier performed the one or more (e.g., each) high-risk or unsafedriving events using the outlier's telematics data. The telematics datareceived from each of the vehicles 210 a-n may include a timestamp ofwhen each sensed and/or otherwise determined data item (e.g., a brakingevent) of the telematics data occurred. The timestamp may include fieldsfor year, month, day, hour, minutes, seconds, sub-seconds, indicationsof before noon or after noon (e.g., am or pm), and/or time-zone. Foreach identified high-risk or unsafe driving event, driving analysisserver 220 may identify the event's associated timestamp from thetelematics data. As an example, a hard-braking event may be associatedwith (e.g., occurred at) a timestamp of Jun. 16, 2015 at 12:31 pm and 5second and 8 tenths of a second, eastern standard time.

In step 504, a computing device (e.g., driving analysis server 220) mayidentify vehicles within the immediate vicinity (e.g., within a presetdistance) of the outlier during a timeframe immediately prior to each ofthe times identified in step 504, so that the computing device maydetermine whether one or more of these vehicles quickly and unexpectedlymoved into the path of the outlier. If so, then an occurrence of suddenswerving or braking event by the outlier may indicate that its driverwas paying proper attention and reacted appropriately to the situationand might not be penalized for performing the high-risk and unsafedriving event.

The preset distance may be any operator specified distance for use indetermining which vehicles are in the immediate vicinity of the outlier.The specified distance may be determined by an operator, insuranceprovider, vehicle manufacturer, or the like. The preset distance may bedynamically adjusted based on the speed or velocity of one or more ofthe vehicles. As an example, the preset distance may be 10 feet when thevehicles are moving less than 20 miles per hour. However, the presetdistance may be 30 feet when the vehicles are moving more than 20 milesper hours. For instance, the driving analysis server 220 may usetelematics data from a camera attached to a traffic structure todetermine whether a vehicle is within the immediate vicinity of anoutlier. For instance, a di stance between two vehicles may bedetermined based on geographic location information (e.g., GPScoordinates) of the vehicles.

The timeframe may be of any specified duration with its end time beingthe timestamp of a high-risk or unsafe driving event. The specifiedduration may be determined by an operator, insurance provider, vehiclemanufacturer, or the like. As an example, if a duration of a timeframeis preset to be 2 seconds, then, in the above hard-braking eventexample, the timeframe may begin at time Jun. 16, 2015 at 12:31 pm and 3second and 8 tenths of a second, eastern standard time and end at timeJun. 16, 2015 at 12:31 pm and 5 second and 8 tenths of a second, easternstandard time. In some embodiments, different types of high-risk orunsafe driving events (e.g., hard-braking events, swerving events, etc.)may be associated with timeframes having different durations. As anexample, the duration of a timeframe associated with a hard-breakingevent may be 2 seconds as discussed above and the duration of atimeframe associated with a swerving event may be 1.5 seconds.

FIG. 7 depicts an illustrative top view of multiple vehicles 710 a-dtraversing a street or road 702, according to one or more aspects of thedisclosure. In this example, driving analysis server 220 may havedetermined that vehicle 710 a is an outlier and that vehicle 710 aperformed a hard-braking event at time Jun. 16, 2015 at 12:31 pm and 5second and 8 tenths of a second, eastern standard time. If the durationof the time is set to be 3 seconds then the timeframe would start attime Jun. 16, 2015 at 12:31 pm and 2 second and 8 tenths of a second,eastern standard time and end at time Jun. 16, 2015 at 12:31 pm and 5second and 8 tenths of a second, eastern standard time.

In this example, driving analysis server 220 may use the telematics data(e.g., velocity data, geographic location, etc.) of vehicles 710 a-d,which may have been received from the vehicles 710, mobile devices,and/or sensors (e.g., cameras) attached to traffic structures 704 (e.g.,traffic lights, stop signs, etc.), to determine, during the timeframe,that the average speed of the vehicles is 45 miles per hour, thatvehicles 710 a-b are separated by a distance 10 feet, that vehicles 710a,c are separated by a distance of 20 feet, that vehicles 710 a,d areseparated by a distance of 60 feet, etc. Driving analysis server 220 maydetermine that the preset distance is 30 feet based on the average speedof the vehicles 710 a-d and may also determine that vehicles 710 b-c areeach within with a preset distance of 30 feet during the timeframe.Driving analysis server 220 may also determine that vehicle 710 d is notwithin the preset of distance of 30 feet. In instances where thegrouping of vehicles is based on proximity, the vehicles in the vicinityof an outlier may be other members of the group.

In step 506, a computing device (e.g., driving analysis server 220) maydetermine whether the vehicles identified as being within the presetdistance during the timeframe (e.g., vehicles 210 b-c) performed ahigh-risk or unsafe driving event during the timeframe that caused theoutlier to perform a high-risk or unsafe driving event. Referring to theexample illustrated in FIG. 7 , driving analysis server 220 maydetermine, using the telematics data from vehicle 710 b or image/videodata from traffic structure 704, that vehicle 710 b performed ahigh-risk or unsafe driving event while within the preset distance ofvehicle 710 a during the timeframe. Namely, vehicle 710 b swerved andcutoff vehicle 710 a causing vehicle 710 a to hard-brake in order toavoid an accident.

In step 508, if a computing device (e.g., driving analysis server 220)determines that a vehicle identified in step 504 performed a high-riskor unsafe driving event that caused the outlier to perform its high-riskor unsafe driving event in step 506, then the computing device maypositively account for or ignore the outlier's high risk or unsafedriving event in its determination of the driver score for the outlier.Following the above example, if vehicle 710 b swerved and cutoff vehicle710 a causing vehicle 710 a to hard-brake in order to avoid an accident,then the hard-brake performed by vehicle 710 a may be positivelyaccounted for or ignored in the determination of the driver score forvehicle 710 a. In instances where a driver score has already beencomputed for vehicle 710 a, driving analysis server 220 may eithermaintain or positively adjust (e.g., increase) the driver score.

In step 510, if a computing device (e.g., driving analysis server 220)determines that a vehicle identified in step 504 either did not performa high-risk or unsafe driving event or that a performed high-risk orunsafe driving event did not cause the outlier to perform its high riskor unsafe driving event in step 506, then the computing device maynegatively account for outlier's high-risk or unsafe driving event indetermining the driver score for the outlier. Referring to and alteringthe example illustrated in FIG. 7 , if vehicle 710 b did not swerve,then the outlier's hard-brake may be negatively accounted for in thedetermination of the driver score for vehicle 710 a. As another example,if vehicle 710 b did swerve but not into the path of vehicle 710 a, thendriving analysis server 220 may determine that the high-risk or unsafedriving event performed by vehicle 710 b did not cause vehicle 710 a tohard-brake and may negatively account for the hard-brake in thedetermination of the driver score for vehicle 710 a. In instances wherea driver score has already been computed for vehicle 710 a, drivinganalysis server 220 may negatively adjust (e.g., decrease) the driverscore. In some cases, the cause of the high-risk or unsafe driving eventperformed by the outlier may be the result of other environmentalcircumstances. As an example, the driving analysis server 220 maydetermine, from telematics data of the outlier received from the outlierand from traffic structure 704 (e.g., a traffic light), that the outlierperformed a hard-brake in response to the traffic structure 704displaying a yellow light. In such an instance, the driver's score mightnot be negatively affected as a result of the hard-brake.

FIG. 8 depicts an illustrative flow diagram for an example method ofdetermining whether a vehicle not involved in an accident caused theaccident and/or otherwise is at fault, according to one or more aspectsof the disclosure. The method of FIG. 8 and/or one or more steps thereofmay be performed by a computing device (e.g., driving analysis server220). The method illustrated in FIG. 8 and/or one or more steps thereofmay be partially or fully embodied, for example, in computer-executableinstructions that are stored in a computer-readable medium, such as anon-transitory computer-readable memory. In some instances, one or moresteps of FIG. 8 may be performed in a different order and/or combined.In some instances, one or more steps of FIG. 8 may be omitted and/orotherwise not performed.

Prior to performing the steps of FIG. 8 , one or more steps of the aboveFIGS. 3-5 may be performed. For example, steps 302-310 may have alreadybeen performed so that the computing device has received the telematicsdata of the vehicles and identified outliers.

The method may being at step 802 in which a computing device (e.g.,driving analysis server 220) may identify a time of an accident andvehicles involved in the accident based on the telematics data of thevehicles 210 a-n. As an example, a sudden shift in linear or angularacceleration and/or linear or angular velocity of a vehicle may indicatethat an accident has occurred. As another example, a sudden shift inlinear or rotational acceleration and/or linear or angular velocity oftwo vehicles substantially simultaneously may also indicate that anaccident has occurred. Once the driving analysis server 220 hasdetermined that an accident has occurred, it may identify a time atwhich the accident occurred from time data (e.g., timestamps) includewith the telematics data of the vehicles.

In step 804, a computing device (e.g., driving analysis server 220) mayidentify vehicles that were not involved in the accident (e.g., did notcollide with any object or vehicle) but were within the immediatevicinity (e.g., within a preset distance) of one or more of the vehiclesinvolved in the accident during a timeframe immediately prior to theaccident. The preset distance may be any operator specified distance(e.g., 10 feet, 20 feet, etc.). Similarly, the duration of the timeframemay be any operator specified timeframe (e.g., 2 seconds, 5 seconds,etc.) with the end time being the time of the accident. Driving analysisserver 220 may identify vehicles within the preset distance during thetimeframe based on telematics data received from those vehicles and/orfrom image/video data received from vehicles involved in the accident.If no vehicles are identified, driving analysis server 220 may determinethat no vehicle outside of those involved in the accident may havecaused the accident. Additionally, driving analysis server 220 mayfurther determine that either the environment or one of the vehiclesinvolved in the accident caused the accident.

Otherwise, if one or more vehicles were identified, then in step 806,driving analysis server 220 may determine whether the one or moreidentified vehicles performed a high-risk or unsafe driving event duringthe timeframe. If not, then in step 812, driving analysis server 220 maydetermine that the identified vehicle did not cause (or was not at faultfor) the accident and that the accident was either caused by theenvironment or by one of the vehicles involved in the accident. If theidentified vehicle did perform a high-risk or unsafe driving event basedon an analysis of either its telematics data or image/video datareceived from another vehicle, then in step 808, the driving analysisserver 220 may determine whether the identified vehicle's high-risk orunsafe driving event caused the accident. As an example, if theidentified vehicle performed a high-risk or unsafe driving event (e.g.,running a red light of a traffic signal) thereby causing a vehicle toswerve and collide with another vehicle or object, driving analysisserver 220 may, in step 810, determine that the identified vehiclecaused (or was at fault for) the accident. Otherwise, if the identifiedvehicle's high-risk or unsafe driving event did not cause the vehicle tocollide with another vehicle or object, then in step 812, the drivinganalysis server 220 may determine that the identified vehicle did notcause the accident and either the environment or the vehicles involvedin the accident caused the accident.

In one or more embodiments, the driver score and/or adjustments to thedriver score may be presented to a driver via a graphical userinterface, which may be provided via a web site or a softwareapplication (e.g., a mobile app). As an example, driving analysis server220 may transmit the score to the driver's mobile device 218 or anothercomputing device (e.g., an onboard computing device of a vehicle,personal computer, laptop, tablet, etc.) for display.

While the aspects described herein have been discussed with respect tospecific examples including various modes of carrying out aspects of thedisclosure, those skilled in the art will appreciate that there arenumerous variations and permutations of the above described systems andtechniques that fall within the spirit and scope of the invention.

The invention claimed is:
 1. A method comprising: receiving telematicsdata from two or more vehicles; determining a time of an accident basedon the telematics data received from the two or more vehicles;determining, from the two or more vehicles, a vehicle that was within apreset distance of the accident; determining whether the vehicleperformed a high-risk driving event based on the telematics datacompared with at least a threshold during a timeframe prior to the timeof the accident; responsive to determining that the vehicle performedthe high-risk driving event during the timeframe prior to the accident,determining whether the high-risk driving event caused the accident;negatively adjust, based on the determination the high-risk drivingevent caused the accident, a driver score of the vehicle, wherein thedriver score is an indication of a driving pattern of the vehiclecompared to a normalized group driving pattern representative of drivingbehavior of the two or more vehicles; positively adjust or maintain,based on the determination the high-risk driving event did not cause theaccident, a driver score of the vehicle; and automatically present thedriver score as (i) an audio output via one or more speakers, (ii) atextual output, audiovisual output, graphical output, or a combinationthereof via a video display device, or (iii) a combination thereof. 2.The method of claim 1, further comprising: determining that the accidentoccurred based on a shift in linear or rotational acceleration of twovehicles of the two or more vehicles.
 3. The method of claim 1, furthercomprising: determining that the accident occurred based on a shift inlinear or angular velocity of two vehicles of the two or more vehicles.4. The method of claim 1, wherein an end time of the timeframe is thetime of the accident.
 5. The method of claim 1, wherein the determiningthe vehicle that was within the preset distance of the accident isperformed based on the telematics data received from the two or morevehicles.
 6. The method of claim 1, further comprises determining thevehicle was not involved in the accident by determining that the vehicledid not collide with any object or vehicle.
 7. The method of claim 1,wherein the vehicle is identified from image/video data received from asecond vehicle involved in the accident.
 8. A non-transitory computerreadable medium storing instructions that, executed by a computingdevice, cause the computing device to: receive telematics data from twoor more vehicles; determine a time of an accident based on thetelematics data received from the two or more vehicles; determine, fromthe two or more vehicles, a vehicle that was within a preset distance ofthe accident; determine whether the vehicle performed a high-riskdriving event based on the telematics data compared with at least athreshold during a timeframe prior to the time of the accident;responsive to determining that the vehicle performed the high-riskdriving event during the timeframe prior to the accident, determiningwhether the high-risk driving event caused the accident; negativelyadjust, based on the determination the high-risk driving event causedthe accident, a driver score of the vehicle, wherein the driver score isan indication of a driving pattern of the vehicle compared to anormalized group driving pattern representative of driving behavior ofthe two or more vehicles; positively adjust or maintain, based on thedetermination the high-risk driving event did not cause the accident, adriver score of the vehicle; and automatically present the adjusteddriver score as (i) an audio output via one or more speakers, (ii) atextual output, audiovisual output, graphical output, or a combinationthereof via a video display device, or (iii) a combination thereof. 9.The non-transitory computer readable medium of claim 8, storinginstructions that, executed by a computing device, cause the computingdevice to: determine that the accident occurred based on a shift inlinear or rotational acceleration of two vehicles of the two or morevehicles.
 10. The non-transitory computer readable medium of claim 8,storing instructions that, executed by a computing device, cause thecomputing device to: determine that the accident occurred based on ashift in linear or angular velocity of two vehicles of the two or morevehicles.
 11. The non-transitory computer readable medium of claim 8,wherein an end time of the timeframe is the time of the accident. 12.The non-transitory computer readable medium of claim 8, wherein thedetermining that the vehicle that was within the preset distance of theaccident is performed based on the telematics data received from the twoor more vehicles.
 13. The non-transitory computer readable medium ofclaim 8, further comprises determining the vehicle that was not involvedin the accident by determining that the vehicle did not collide with anyobject or vehicle.
 14. The non-transitory computer readable medium ofclaim 8, wherein the vehicle is identified from image/video datareceived from a second vehicle involved in the accident.
 15. Anapparatus comprising: one or more processors; and memory storinginstructions that, executed by the one or more processors, cause theapparatus to: receive telematics data from two or more vehicles;determine a time of an accident based on the telematics data receivedfrom the two or more vehicles; determine, from the two or more vehicles,a vehicle that was within a preset distance of the accident; determinewhether the vehicle performed a high-risk driving event based on thetelematics data compared with at least a threshold during a timeframeprior to the time of the accident; responsive to determining that thevehicle performed the high-risk driving event during the timeframe priorto the accident, determining whether the high-risk driving event causedthe accident; negatively adjust, based on the determination thehigh-risk driving event caused the accident, a driver score of thevehicle, wherein the driver score is an indication of a driving patternof the vehicle compared to a normalized group driving patternrepresentative of driving behavior of the two or more vehicles;positively adjust or maintain, based on the determination the high-riskdriving event did not cause the accident, a driver score of the vehicle;and automatically present the adjusted driver score as (i) an audiooutput via one or more speakers, (ii) a textual output, audiovisualoutput, graphical output, or a combination thereof via a video displaydevice, or (iii) a combination thereof.
 16. The apparatus of claim 15,the memory storing instructions that, executed by the one or moreprocessors, cause the apparatus to: determine that the accident occurredbased on a shift in linear or rotational acceleration of two vehicles ofthe two or more vehicles.
 17. The apparatus of claim 15, the memorystoring instructions that, executed by the one or more processors, causethe apparatus to: determine that the accident occurred based on a shiftin linear or angular velocity of two vehicles of the two or morevehicles.
 18. The apparatus of claim 15, wherein an end time of thetimeframe is the time of the accident.
 19. The apparatus of claim 15,wherein the determining the vehicle that was within the preset distanceof the accident is performed based on the telematics data received fromthe two or more vehicles.
 20. The apparatus of claim 15, furthercomprises determining the vehicle that was not involved in the accidentby determining that the vehicle did not collide with any object orvehicle.